It’s been a long time since I taught my first big lecture course, and at this point, I’ve got a system that I think works well. My students take notes while I lecture, but they also spend lots of class time working in small groups solving problems and answering meaty questions.

Machine learning is capable of amazing things. Speech recognition was a fragile novelty 15 years ago and now it’s ubiquitous. Self driving cars are on the verge of breaking through. Chess and Go are now mastered by machines. At the same time we are gathering unprecedented amounts of data on our students. We track their behavior in class and their usage of the Learning Management System (LMS) outside class. We measure their performance through exam scores, quiz scores, answers to in-class questions, and evaluations of their writing. To supplement this information, we have demographics, surveys, and measures of their performance in other classes. It seems obvious that combining these two technologies should yield important insights into student learning, and in fact big money is being invested by the smallest and biggest edtech companies to do exactly this. And I think it’s really dangerous.

This semester I’m teaching two big classes, and for each, I’m giving two midterms and a final. All six of these exams are composed entirely of free response questions. Some questions require calculations, some require interpretations, and some require longer explanations. You wouldn’t think I’d have much use for an app like ZipGrade that’s designed to grade multiple choice quizzes, but you’d be dead wrong.

Pretty much anyone who has talked to me recently has heard me sing the praises of invention activities. These differ from more typical in-class activities in that students are asked to grapple with challenging problems BEFORE they are taught how to solve them. The experimental work of Dan Schwartz and colleagues shows that this struggle prepares students well to learn from the lecture.